BOARD # 293: Reflection on Outcomes Data from Eight Years of a Summer REU Site in Systems Bioengineering and Biomedical Data Sciences

Timothy E. Allen

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Conference: 2025 ASEE Annual Conference & Exposition
Location: Montreal, Quebec, Canada
Publication Date: June 22, 2025
Start Date: June 22, 2025
End Date: August 15, 2025
Conference Session: NSF Grantees Poster Session I
Tagged Topics: Diversity and NSF Grantees Poster Session
Page Count: 6
Permanent URL: https://peer.asee.org/55658

Paper Authors

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Timothy E. Allen University of Virginia orcid.org/0000-0001-5768-6339

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Dr. Timothy E. Allen is a Professor in the Department of Biomedical Engineering at the University of Virginia. He received a B.S.E. in Biomedical Engineering at Duke University and M.S. and Ph.D. degrees in Bioengineering at the University of California, San Diego. Dr. Allen's teaching activities include coordinating the undergraduate teaching labs and the Capstone Design sequence in the BME department at the University of Virginia, and his research interests are in the fields of computational biology and bioinformatics. He is also interested in evaluating the pedagogical approaches optimal for teaching lab concepts and skills, computational modeling approaches, and professionalism within design classes. He is active within the Biomedical Engineering Division of the American Society for Engineering Education and previously served on the executive committee of this division (Program Chair 2011, Division Chair 2012, & Nominating Committee Chair 2013). Dr. Allen is a fellow in the American Institute for Medical and Biological Engineering (AIMBE) College of Fellows, and he has received twelve teaching awards and honors at UVA, including the All-University Teaching Award and the Thomas E. Hutchinson Faculty Award for Dedication and Excellence in Teaching. Since 2016, he has been the PI on an NSF REU site in multi-scale systems bioengineering and biomedical data sciences, a pan-university collaboration involving faculty from four schools at UVA, as well as six partner institutions in the mid-Atlantic and Southeast.

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Abstract

The biotechnology and pharmaceutical industries are increasingly reliant on a workforce pipeline of graduates possessing the skills needed to quantitatively describe complex systems to predict functional outcomes relevant to healthy physiological function and to disease states. These skills will be essential for not only identifying novel drug targets and ascertaining the etiology of complex diseases such as cancer and heart disease, but also for achieving truly personalized medical diagnostics, therapies, and surgical approaches toward treating these diseases. Moreover, inherent biological complexity and high-throughput measurement approaches lead to massive “big data” sets, often with thousands of heterogeneous values. This complexity requires data science tools such as data-driven modeling and machine learning to appropriately integrate heterogeneous data. Thus, it is imperative to train a diverse new generation of scientists in the concepts and practice of multi-scale systems bioengineering and biomedical data sciences (BDS) research.

At the University of Virginia, we developed an NSF-funded REU Site in Multi-Scale Systems Bioengineering and BDS (NSF #1560282 & #1950374) that has supported 81 students engaging in research projects for the past eight years (2017-2024). These students were recruited out of a total of 1,375 applicants, with participants drawn from 54 colleges and universities. Two summers (2020 & 2021) the program was run as a virtual REU due to institutional constraints on visiting researchers due to the pandemic. The REU students were matched to a mentor based on a combination of student interest in specific sub-areas of systems bioengineering research and mentor availability each summer. Most research projects relied primarily on previously developed methods and tools and typically involved application to biological data and generation of testable hypotheses. The specific research projects included a wide variety of topics in the field, ranging anywhere from molecular scale biophysics models to cell-scale signaling models, biomedical data science analysis of genetic data, tissue-level biomechanics models, and image analysis algorithms for quantifying cell distribution in tissue-engineered constructs. The participants took part in an introductory bootcamp on the fundamentals of systems modeling and had multiple opportunities to present their research progress throughout the summer to experts in the field. They also received professional development training on research ethics, technical communication, and launching careers in systems bioengineering. We analyzed participant demographics, outcomes in presenting or publishing their work, career outcomes, and survey data from each summer’s cohort.

The 81 REU participants came from 54 colleges and universities and represented 24 different majors, with 47% of them biomedical engineering (BME) majors; 68% were from groups traditionally underrepresented in STEM, 32% were first-generation students; 58% were women; and 41% attended non-R1 institutions; 67% presented their work at national meetings, and nine have become co-authors on ten papers. Of the 60 who have since graduated, 85% are either in graduate school or in STEM industry positions. Post-REU surveys of participants revealed that 98% of respondents rated their overall experience with the REU as either “very satisfied” or “satisfied” (average 4.72 on a Likert scale). Evaluations of specific program objectives and mentoring were similarly high. Regarding impact on long-term goals, nearly 75% said that the REU increased their interest in STEM and encouraged them to pursue further education towards a research or academic career, while 45% said the program helped solidify interest specifically in systems bioengineering.

From a programmatic standpoint, we have several recommendations: Our large number of applications suggest that the specific research area is important, and if well-presented to possible applicants can be a highly motivating selling point. Our experience during the pandemic was that a virtual REU can lead to positive research outcomes, although cohort bonding and the experience of working in a lab are diminished. One challenge of a one-size-fits-all bootcamp is appropriately accommodating the needs of the varied research topics. Some projects require coding, but others do not since they use established software tools; some require model development, others data science. We are continually iterating to find the optimal balance of instruction in topics that support every student in the program.

Citation
Format

Allen, T. E. (2025, June), BOARD # 293: Reflection on Outcomes Data from Eight Years of a Summer REU Site in Systems Bioengineering and Biomedical Data Sciences Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/55658

TY - CPAPER
AB - The biotechnology and pharmaceutical industries are increasingly reliant on a workforce pipeline of graduates possessing the skills needed to quantitatively describe complex systems to predict functional outcomes relevant to healthy physiological function and to disease states. These skills will be essential for not only identifying novel drug targets and ascertaining the etiology of complex diseases such as cancer and heart disease, but also for achieving truly personalized medical diagnostics, therapies, and surgical approaches toward treating these diseases. Moreover, inherent biological complexity and high-throughput measurement approaches lead to massive “big data” sets, often with thousands of heterogeneous values. This complexity requires data science tools such as data-driven modeling and machine learning to appropriately integrate heterogeneous data. Thus, it is imperative to train a diverse new generation of scientists in the concepts and practice of multi-scale systems bioengineering and biomedical data sciences (BDS) research.

At the University of Virginia, we developed an NSF-funded REU Site in Multi-Scale Systems Bioengineering and BDS (NSF #1560282 &amp;amp; #1950374) that has supported 81 students engaging in research projects for the past eight years (2017-2024). These students were recruited out of a total of 1,375 applicants, with participants drawn from 54 colleges and universities. Two summers (2020 &amp;amp; 2021) the program was run as a virtual REU due to institutional constraints on visiting researchers due to the pandemic. The REU students were matched to a mentor based on a combination of student interest in specific sub-areas of systems bioengineering research and mentor availability each summer. Most research projects relied primarily on previously developed methods and tools and typically involved application to biological data and generation of testable hypotheses. The specific research projects included a wide variety of topics in the field, ranging anywhere from molecular scale biophysics models to cell-scale signaling models, biomedical data science analysis of genetic data, tissue-level biomechanics models, and image analysis algorithms for quantifying cell distribution in tissue-engineered constructs. The participants took part in an introductory bootcamp on the fundamentals of systems modeling and had multiple opportunities to present their research progress throughout the summer to experts in the field. They also received professional development training on research ethics, technical communication, and launching careers in systems bioengineering. We analyzed participant demographics, outcomes in presenting or publishing their work, career outcomes, and survey data from each summer’s cohort.

The 81 REU participants came from 54 colleges and universities and represented 24 different majors, with 47% of them biomedical engineering (BME) majors; 68% were from groups traditionally underrepresented in STEM, 32% were first-generation students; 58% were women; and 41% attended non-R1 institutions; 67% presented their work at national meetings, and nine have become co-authors on ten papers. Of the 60 who have since graduated, 85% are either in graduate school or in STEM industry positions. Post-REU surveys of participants revealed that 98% of respondents rated their overall experience with the REU as either “very satisfied” or “satisfied” (average 4.72 on a Likert scale). Evaluations of specific program objectives and mentoring were similarly high. Regarding impact on long-term goals, nearly 75% said that the REU increased their interest in STEM and encouraged them to pursue further education towards a research or academic career, while 45% said the program helped solidify interest specifically in systems bioengineering.

From a programmatic standpoint, we have several recommendations: Our large number of applications suggest that the specific research area is important, and if well-presented to possible applicants can be a highly motivating selling point. Our experience during the pandemic was that a virtual REU can lead to positive research outcomes, although cohort bonding and the experience of working in a lab are diminished. One challenge of a one-size-fits-all bootcamp is appropriately accommodating the needs of the varied research topics. Some projects require coding, but others do not since they use established software tools; some require model development, others data science. We are continually iterating to find the optimal balance of instruction in topics that support every student in the program.
AU - Timothy E. Allen
CY - Montreal, Quebec, Canada
DA - 2025/06/22
PB - ASEE Conferences
TI - BOARD # 293: Reflection on Outcomes Data from Eight Years of a Summer REU Site in Systems Bioengineering and Biomedical Data Sciences
UR - https://peer.asee.org/55658
ER -